Research in biologically-inspired optimisation has been fl<;lurishing over the past decades. This approach adopts a bott0!ll-up viewpoint to understand and mimic certain features of a biological system. It has been proved useful in developing nondeterministic algorithms, such as Evolutionary Algorithms (EAs) and Swarm Intelligence (SI). Bacteria, as the simplest creature in nature, are of particular interest in recent studies. In the past thousands of millions of years, bacteria have exhibited a self-organising behaviour to cope with the natural selection. For example, bacteria have developed a number of strategies to search for food sources with a very efficient manner. This thesis explores the potential of understanding of a biological system by modelling the' underlying mechanisms of bacterial foraging patterns and investigates their applicability to engineering optimisation problems. :rvlodelling plays a significant role in understanding bacterial foraging behaviour. Mathematical expressions and experimental observations have been utilised to represent biological systems. However, difficulties arise from the lack of systematic analysis of the developed models and experimental data. Recently, Systems Biology has be,en proposed to overcome this barrier, with the effort from a number of research fields, including Computer Science and Systems Engineering. At the same time, Individual-based Modelling (IbM) has emerged to assist the modelling of a biological system. Starting from a basic model of foraging and proliferation of bacteria, the development of an IbM of bacterial systems of this thesis focuses on a Varying Environment BActerial Model (VEBAM). Simulation results demonstrate that VEBAM is able to provide a new perspective to describe interactions between the bacteria and their food environment. Knowledge transfer from modelling of bacterial systems to solving optimisation problems also composes an important part of this study. Three Bacteriainspired Algorithms (BalAs) have been developed to bridge the gap between modelling and optimisation. These algorithms make use of the. self-adaptability of individual bacteria in the group searching activities described in VEBAM, while incorporating a variety of additional features. In particular, the new bacterial foraging algorithm with varying population (BFAVP) takes bacterial metabolism into consideration. The group behaviour in Particle Swarm Optimiser (PSO) is adopted in Bacterial Swarming Algorithm (BSA) to enhance searching ability. To reduce computational time, another algorithm, a Paired-bacteria Optimiser (PBO) is designed specifically to further explore the capability of BalAs. Simulation studies undertaken against a wide range of benchmark functions demonstrate a satisfying performance with a reasonable convergence speed. To explore the potential of bacterial searching ability in optimisation undertaken in a varying environment, a dynamic bacterial foraging algorithm (DBFA) is developed with the aim of solving optimisation in a time-varying environment. In this case, the balance between its convergence and exploration abilities is investigated, and a new scheme of reproduction is developed which is different froin that used for static optimisation problems. The simulation studies have been undertaken and the results show that the DBFA can adapt to various environmental changes rapidly. One of the challenging large-scale complex optimisation problems is optimal power flow (OPF) computation. BFAVP shows its advantage in solving this problem. A simulation study has been performed on an IEEE 30-bus system, and the results are compared with PSO algorithm and Fast Evolutionary Programming (FEP) algorithm, respectively. Furthermore, the OPF problem is extended for consideration in varying environments, on which DBFA has been evaluated. A simulation study has been undertaken on both the IEEE 30-bus system and the IEEE l1S-bus system, in compariso~ with a number of existing algorithms. The dynamic OPF problem has been tackled for the first time in the area of power systems, and the results obtained are encouraging, with a significant amount of energy could possibly being saved. Another application of BaIA in this thesis is concerned with estimating optimal parameters of a power transformer winding model using BSA. Compared with Genetic Algorithm (GA), BSA is able to obtain a more satisfying result in modelling the transformer winding, which could not be achieved using a theoretical transfer function model
